WiFi networks are known to be a cost-efficient traffic offloading solution for mobile networks. The Multi Access Packet Data Network Connectivity is a feature introduced in LTE Release 10 in order to allow users to be simultaneously connected to multiple radio access networks (RAN). Although this feature brings many advantages, such as the possibility to implement QoS-based traffic steering, it poses also many challenges, one of which is distributing traffic among the two radio access technologies. In this paper, we propose a traffic- aware user association algorithm for heterogeneous LTE/WiFi RANs. The proposed algorithm is formulated as an Integer Linear Programming (ILP) problem jointly optimizing user association and resource allocation. A heuristic is also proposed in order to address the scalability issues of the ILP-based algorithm. Numerical simulations are used in order to compare the proposed approaches. Finally, we implemented and tested the heuristic in small-scale testbed using the 5G-EmPOWER platform.

Traffic-Aware User Association in Heterogeneous LTE/WiFi Radio Access Networks

Roberto Riggio
2018

Abstract

WiFi networks are known to be a cost-efficient traffic offloading solution for mobile networks. The Multi Access Packet Data Network Connectivity is a feature introduced in LTE Release 10 in order to allow users to be simultaneously connected to multiple radio access networks (RAN). Although this feature brings many advantages, such as the possibility to implement QoS-based traffic steering, it poses also many challenges, one of which is distributing traffic among the two radio access technologies. In this paper, we propose a traffic- aware user association algorithm for heterogeneous LTE/WiFi RANs. The proposed algorithm is formulated as an Integer Linear Programming (ILP) problem jointly optimizing user association and resource allocation. A heuristic is also proposed in order to address the scalability issues of the ILP-based algorithm. Numerical simulations are used in order to compare the proposed approaches. Finally, we implemented and tested the heuristic in small-scale testbed using the 5G-EmPOWER platform.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/291199
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